1. Field
The present specification generally relates to image processing systems and, more particularly, to image processing systems for compressing and decompressing images.
2. Technical Background
An image processing system may compress an image to form a compressed representation of the image and/or decompress a compressed representation of the image to reconstruct the image. It may be desirable for an image processing system, such as a large-scale surveillance image processing system to compress and/or decompress an image in real time. When algorithmically complex compression and/or decompression algorithms are utilized to compress and/or decompress large images in real time, it may be desirable for the image processing system to employ fast compression and/or decompression algorithms.
For example, an image processing system may employ JPEG 2000 compression and/or decompression. JPEG 2000 algorithms may result in enhanced compression efficiency compared to other compression algorithms. In order to achieve such enhanced compression efficiency, JPEG 2000 compression algorithms may be substantially more algorithmically complex than other image compression algorithms, such as JPEG compression algorithms. Similarly, JPEG 2000 decompression algorithms may be substantially more algorithmically complex than other image decompression algorithms, such as JPEG decompression algorithms.
A typical image compression algorithm may employ floating point-based wavelet transform, quantization, and encoding steps. The wavelet transform may use floating point computation to transform integer pixel values into floating point wavelet coefficients that represent the original image. The quantization step may use floating point computation to modify the floating point wavelet coefficients so that the modified wavelet coefficients represent the original image with the least amount of precision required to represent the image with a desired image quality after reconstruction. The encoding step is applied to represent the quantized wavelet coefficients in an efficient way in order to achieve further compression. As part of the encoding step, distortion estimates, which are used subsequently in the image compression algorithm, may be generated using floating point processing. Image compression algorithms that utilize such floating point computation during the wavelet transform, the quantization step, and distortion estimation may be computationally intensive and may extend the time required for image compression.
Accordingly, a need exists for alternative image processing systems.